Health risk assessments due to nitrate levels in drinking water in villages of Azadshahr,northeastern Iran

Groundwater is the main water source used for drinking and cooking purposes globally. Nitrate level in most groundwater resources in arid and semi-arid areas has increased in the past several decades as a result of human activities and natural processes. This may exert a great impact on human health. To learn the contamination circumstances of groundwater nitrate in villages of Azadshahr, Iran and assess its probable risk to the health of adults, children and infants, fifty-eight groundwater samples were collected from wells and springs in 2018. Nitrate concentrations had a wide spatial variability in wells and springs of the studied villages, with values going from 1 up to 51 mg/L. Exceedances of the EPA standard value were limited to two village springs (villages Nili and Narab, with nitrate level of 51 and 46 mg/L, respectively). The hazard quotients (HQ) values for 41% of children and infants were above the safety level (i.e., HQ?>?1), suggesting that groundwater nitrate would have significant health effects on these age groups. Therefore, appropriate control measures and sanitation improvement programs should be put in place to protect the health of the residents in the contaminated villages.     

Transition from I‐type to A‐type magmatism in the Sanandaj–Sirjan Zone,NW Iran: an extensional intra‐continental arc

The South Dehgolan pluton, in NW Iran was emplaced into the Sanandaj–Sirjan magmatic–metamorphic zone. This composite intrusion comprises three main groups: (1) monzogabbro–monzodiorite rocks, (2) quartz monzonite–syenite rocks, and (3) a granite suite which crops out in most of the area. The granites generally show high SiO₂ content from 72.1%–77.6 wt.% with diagnostic mineralogy consisting of biotite and amphibole along the boundaries of feldspar–quartz crystals which implies anhydrous primary magma compositions. The granite suite is metaluminous and distinguished by high FeOt/MgO ratios (av. 9.6 wt.%), typical of ferroan compositions with a pronounced A‐type affinity with high Na₂O + K₂O contents, high Ga/Al ratios, enrichment in Zr, Nb, REE, and depletion in Eu. The quartz monzonite–syenites show intermediate SiO₂ levels (59.8%–64.5 wt.%) with metaluminous, magnesian to ferroan characteristics, intermediate Na₂O + K₂O contents, enrichment in Zr, Nb, REE, Ga/Al, and depletion in Eu. The monzogabbro–monzodiorites show overall lower SiO₂ content (48.5%–55.9 wt.%) with metaluminous and calc‐alkaline compositions, relatively lower Na₂O + K₂O contents, low Ga/Al ratios, and FeOt/MgO (av. 1.6 wt.%) ratios, low abundances of Zr, Nb, and lower REE element concentrations relative to the granites and quartz monzonite–syenites. These geochemical differences among the three different rocks suites are likely to indicate different melt origins. We suggest that the South Dehgolan pluton resulted from a change in the geodynamic regime, from compression to extension in the Sanandaj–Sirjan zone during Mesozoic subduction of the Neo‐Tethys oceanic crust beneath the Central Iranian microcontinent. Copyright © 2015 John Wiley & Sons, Ltd.     

Time trend analysis of some agroclimatic variables during the last half century over Iran

Theoretical and Applied Climatology - We assessed the trends of precipitation, maximum and minimum temperature (Tmax and Tmin), diurnal temperature range (DTR), water requirement of autumn-planted...     

The accountability imperative for quantifying the uncertainty of emission forecasts: evidence from Mexico

Governmental climate change mitigation targets are typically developed with the aid of forecasts of greenhouse-gas (GHG) emissions. The robustness and credibility of such forecasts depends, among other issues, on the extent to which forecasting approaches can reflect prevailing uncertainties. We apply a transparent and replicable method to quantify the uncertainty associated with projections of gross domestic product growth rates for Mexico, a key driver of GHG emissions in the country. We use those projections to produce probabilistic forecasts of GHG emissions for Mexico. We contrast our probabilistic forecasts with Mexico’s governmental deterministic forecasts. We show that, because they fail to reflect such key uncertainty, deterministic forecasts are ill-suited for use in target-setting processes. We argue that (i) guidelines should be agreed upon, to ensure that governmental forecasts meet certain minimum transparency and quality standards, and (ii) governments should be held accountable for the appropriateness of the forecasting approach applied to prepare governmental forecasts, especially when those forecasts are used to derive climate change mitigation targets.

POLICY INSIGHTS

• No minimum transparency and quality standards exist to guide the development of GHG emission scenario forecasts, not even when these forecasts are used to set national climate change mitigation targets.

• No accountability mechanisms appear to be in place at the national level to ensure that national governments rely on scientifically sound processes to develop GHG emission scenarios.

• Using probabilistic forecasts to underpin emission reduction targets represents a scientifically sound option for reflecting in the target the uncertainty to which those forecasts are subject, thus increasing the validity of the target.

• Setting up minimum transparency and quality standards, and holding governments accountable for their choice of forecasting methods could lead to more robust emission reduction targets nationally and, by extension, internationally.

     

Magnesium Isotopic Compositions of International Geological Reference Materials

Magnesium isotopic compositions are reported for twenty‐four international geological reference materials including igneous, metamorphic and sedimentary rocks, as well as phlogopite and serpentine minerals. The long‐term reproducibility of Mg isotopic determination, based on 4‐year analyses of olivine and seawater samples, was ≤ 0.07‰ (2s) for δ²⁶Mg and ≤ 0.05‰ (2s) for δ²⁵Mg. Accuracy was tested by analysis of synthetic reference materials down to the quoted long‐term reproducibility. This comprehensive dataset, plus seawater data produced in the same laboratory, serves as a reference for quality assurance and inter‐laboratory comparison of high‐precision Mg isotopic data.     

Calcareous nannofossils from chalky limestone of upper Abderaz Formation and lower part of Abtalkh Formation in the Kopet-Dogh range NE Iran

For the first time, the calcareous nannofossils of the chalky limestone of upper Abderaz Formation and lower part of Abtalkh Formation have been studied. In this study, 83 nannoplanktonic species of 45 genera were identified and presented. A biostratigraphic study of calcareous nannofossils from this section has allowed the recognition of five calcareous nannofossil biozones of Sissingh (Geol Mijnbouw 56:37–65, 1977) CC17–CC21. On the obtained calcareous nannofossils, the age of this section is Late Santonian/Early Campanian–Early Late Campanian.     

Model updating and seismic loss assessment for a portfolio of bridges

The majority of bridge infrastructures in Italy were built in the 1960s and ‘70s without any specific seismic provision being made. As a consequence, it is expected that these bridges would be highly vulnerable if subjected to a significant seismic event. Given this background, it is natural that the rapid and accurate assessment of economic losses incurred to the bridge infrastructure as a result of such an event could play a crucial role in emergency management in the immediate aftermath of an earthquake. Focusing on the infrastructure system of highway bridges in the Campania region in Italy, this paper demonstrates how both state-of-the-art methodologies in portfolio loss assessment and the available data can be used to assess the probability distribution of the repair costs incurred due to the 1980 Irpinia earthquake. Formulating a probabilistic loss assessment for a portfolio of bridges as a standard Monte Carlo simulation problem helps to resolve the spatial risk integral efficiently. One of the specific features of this case study is the use of statistical methods for updating models of: (a) ground motion predictions, (b) vulnerability/fragility and (c) exposure/costs, based on the available data. It has been observed that alternative hypotheses concerning the ground motion correlation structure can significantly affect the distribution of direct economic losses. Furthermore, updating the ground motion prediction based on available recordings may significantly reduce the dispersion in the estimate of the direct economic losses.     

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO,BAT and COA algorithms

In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.     

Assessment of Empirical Pressure–Volume–Temperature Correlations in Gas Condensate Reservoir Fluids: Case Studies

Natural Resources Research - Measurement and modeling of fluid properties and phase behavior of gas condensate reservoir fluids are challenging tasks. Many researchers proposed various empirical...